{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "'''\n",
    "【课程1.4】  统计分析\n",
    "\n",
    "统计指标对定量数据进行统计描述，常从集中趋势和离中趋势两个方面进行分析\n",
    "\n",
    "集中趋势度量 / 离中趋势度量\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          f  value\n",
      "0  0.006347    109\n",
      "1  0.011855    111\n",
      "2  0.003533    113\n",
      "3  0.001222    116\n",
      "4  0.018974    109\n",
      "------\n",
      "简单算数平均值为：109.27\n",
      "加权算数平均值为：108.46\n"
     ]
    }
   ],
   "source": [
    "# 1、集中趋势度量\n",
    "# 指一组数据向某一中心靠拢的倾向，核心在于寻找数据的代表值或中心值 —— 统计平均数\n",
    "# 算数平均数、位置平均数\n",
    "# （1）算数平均数\n",
    "\n",
    "data = pd.DataFrame({'value':np.random.randint(100,120,100),\n",
    "                    'f':np.random.rand(100)})\n",
    "data['f'] = data['f'] / data['f'].sum()  # f为权重，这里将f列设置成总和为1的权重占比\n",
    "print(data.head())\n",
    "print('------')\n",
    "# 创建数据\n",
    "\n",
    "mean = data['value'].mean()\n",
    "print('简单算数平均值为：%.2f' % mean)\n",
    "# 简单算数平均值 = 总和 / 样本数量 （不涉及权重）\n",
    "\n",
    "mean_w = (data['value'] * data['f']).sum() / data['f'].sum()\n",
    "print('加权算数平均值为：%.2f' % mean_w)\n",
    "# 加权算数平均值 = (x1f1 + x2f2 + ... + xnfn) / (f1 + f2 + ... + fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "众数为 [112]\n",
      "中位数为109\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xb550a58>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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neeLePcY6D/MuYIaIvCIiHwITgN7GmPP8pt8BfCMiU0Tky+LnB4GRfuu8EWtP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\nMwf++AMeecSb3r49/PyzGi59+/qOlrz3Hlx3nX5evVpD8fr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      "text/plain": [
       "<matplotlib.figure.Figure at 0x9825898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1、集中趋势度量\n",
    "# （2）位置平均数\n",
    "\n",
    "m = data['value'].mode()\n",
    "print('众数为',m.tolist())\n",
    "# 众数是一组数据中出现次数最多的数，这里可能返回多个值\n",
    "\n",
    "med = data['value'].median()\n",
    "print('中位数为%i' % med)\n",
    "# 中位数指将总体各单位标志按照大小顺序排列后，中间位置的数字\n",
    "\n",
    "data['value'].plot(kind = 'kde',style = '--k',grid = True)\n",
    "# 密度曲线\n",
    "\n",
    "plt.axvline(mean,hold=None,color='r',linestyle=\"--\",alpha=0.8)  \n",
    "plt.text(mean + 5,0.005,'简单算数平均值为：%.2f' % mean, color = 'r')\n",
    "# 简单算数平均值\n",
    "\n",
    "plt.axvline(mean_w,hold=None,color='b',linestyle=\"--\",alpha=0.8)  \n",
    "plt.text(mean + 5,0.01,'加权算数平均值：%.2f' % mean_w, color = 'b')\n",
    "# 加权算数平均值\n",
    "\n",
    "plt.axvline(med,hold=None,color='g',linestyle=\"--\",alpha=0.8)  \n",
    "plt.text(mean + 5,0.015,'中位数：%i' % med, color = 'g')\n",
    "# 中位数\n",
    "# **这里三个数text显示的横坐标一致，目的是图示效果不拥挤"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                A_sale      B_sale\n",
      "2017-06-01  794.068980  531.142147\n",
      "2017-06-02   99.239344  724.638103\n",
      "2017-06-03  337.092146  423.990829\n",
      "2017-06-04  609.858618  738.671698\n",
      "2017-06-05  900.874106  525.249483\n",
      "------\n"
     ]
    }
   ],
   "source": [
    "# 2、离中趋势度量\n",
    "# 指一组数据中各数据以不同程度的距离偏离中心的趋势\n",
    "# 极差与分位差、方差与标准差、离散系数\n",
    "\n",
    "data = pd.DataFrame({'A_sale':np.random.rand(30)*1000,\n",
    "                    'B_sale':np.random.rand(30)*1000},\n",
    "                   index = pd.period_range('20170601','20170630'))\n",
    "print(data.head())\n",
    "print('------')\n",
    "# 创建数据\n",
    "# A/B销售额量级在同一水平"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                A_sale      B_sale\n",
      "2017-06-01  994.808396  128.929773\n",
      "2017-06-02  708.276945  670.378958\n",
      "2017-06-03  386.549297  334.349566\n",
      "2017-06-04  478.059490  381.773828\n",
      "2017-06-05  612.560799  475.183995\n",
      "------\n",
      "A销售额的极差为：990.78, B销售额的极差为：921.67\n",
      "------\n",
      "A销售额的分位差为：358.99, B销售额的分位差为：439.30\n",
      "------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xba32710>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5Q6MFBMTStIiFrCEWsoZYyBryhkYLAAAAAAKj0QIAAACAwGi0gIBWrFiRugQUBFlDLGQN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\nNe7EHRcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xba57048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2、离中趋势度量\n",
    "# （1）极差、分位差\n",
    "\n",
    "data = pd.DataFrame({'A_sale':np.random.rand(30)*1000,\n",
    "                    'B_sale':np.random.rand(30)*1000},\n",
    "                   index = pd.period_range('20170601','20170630'))\n",
    "print(data.head())\n",
    "print('------')\n",
    "# 创建数据\n",
    "# A/B销售额量级在同一水平\n",
    "\n",
    "a_r = data['A_sale'].max() - data['A_sale'].min()\n",
    "b_r = data['B_sale'].max() - data['B_sale'].min()\n",
    "print('A销售额的极差为：%.2f, B销售额的极差为：%.2f' % (a_r,b_r))\n",
    "print('------')\n",
    "# 极差\n",
    "# 没有考虑中间变量的变动，测定离中趋势不稳定\n",
    "\n",
    "sta = data['A_sale'].describe()\n",
    "stb = data['B_sale'].describe()\n",
    "#print(sta)\n",
    "a_iqr = sta.loc['75%'] - sta.loc['25%']\n",
    "b_iqr = stb.loc['75%'] - stb.loc['25%']\n",
    "print('A销售额的分位差为：%.2f, B销售额的分位差为：%.2f' % (a_iqr,b_iqr))\n",
    "print('------')\n",
    "# 分位差\n",
    "\n",
    "color = dict(boxes='DarkGreen', whiskers='DarkOrange', medians='DarkBlue', caps='Gray')\n",
    "data.plot.box(vert=False,grid = True,color = color,figsize = (10,3))\n",
    "# 箱型图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A销售额的标准差为：255.51, B销售额的标准差为：260.38\n",
      "A销售额的方差为：65287.16, B销售额的方差为：67796.90\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0xbb4ae10>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Wtsc9tiVZFlvewn/trv5ne6T/vBbegMG1eLeF2gA85X/mtwDzgSvwvg3ZJy6GexO2Wd4j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      "text/plain": [
       "<matplotlib.figure.Figure at 0xba52a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2、离中趋势度量\n",
    "# （2）方差与标准差\n",
    "\n",
    "a_std = sta.loc['std']\n",
    "b_std = stb.loc['std']\n",
    "a_var = data['A_sale'].var()\n",
    "b_var = data['B_sale'].var()\n",
    "print('A销售额的标准差为：%.2f, B销售额的标准差为：%.2f' % (a_std,b_std))\n",
    "print('A销售额的方差为：%.2f, B销售额的方差为：%.2f' % (a_var,b_var))\n",
    "# 方差 → 各组中数值与算数平均数离差平方的算术平均数\n",
    "# 标准差 → 方差的平方根\n",
    "# 标准差是最常用的离中趋势指标 → 标准差越大，离中趋势越明显\n",
    "\n",
    "fig = plt.figure(figsize = (12,4))\n",
    "ax1 = fig.add_subplot(1,2,1)\n",
    "data['A_sale'].plot(kind = 'kde',style = 'k--',grid = True,title = 'A密度曲线')\n",
    "plt.axvline(sta.loc['50%'],hold=None,color='r',linestyle=\"--\",alpha=0.8)  \n",
    "plt.axvline(sta.loc['50%'] - a_std,hold=None,color='b',linestyle=\"--\",alpha=0.8)  \n",
    "plt.axvline(sta.loc['50%'] + a_std,hold=None,color='b',linestyle=\"--\",alpha=0.8)  \n",
    "# A密度曲线，1个标准差\n",
    "\n",
    "ax2 = fig.add_subplot(1,2,2)\n",
    "data['B_sale'].plot(kind = 'kde',style = 'k--',grid = True,title = 'B密度曲线')\n",
    "plt.axvline(stb.loc['50%'],hold=None,color='r',linestyle=\"--\",alpha=0.8)  \n",
    "plt.axvline(stb.loc['50%'] - b_std,hold=None,color='b',linestyle=\"--\",alpha=0.8)  \n",
    "plt.axvline(stb.loc['50%'] + b_std,hold=None,color='b',linestyle=\"--\",alpha=0.8)  \n",
    "# B密度曲线，1个标准差"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
